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PWWP domain-containing proteins play a pivotal role in chromatin-mediated biological processes, and their aberrant regulation is linked to various human diseases. Recent years have witnessed remarkable strides in unraveling the structural and functional features of PWWP domain-containing proteins, propelling significant advances in targeting the PWWP domain-containing proteins for drug discovery purposes. Several drugs have already been approved, while others are currently in clinical trials. This review offers a comprehensive overview of the latest developments on PWWP domain-containing proteins, including their structural characteristics and biological significance. It also provides detailed insights into the drug discovery process targeting these proteins, including screening, design, and structural optimization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Predicting protein-ligand binding affinity is a crucial and challenging task in structure-based drug discovery. With the accumulation of complex structures and binding affinity data, various machine-learning scoring functions, particularly those based on deep learning, have been developed for this task, exhibiting superiority over their traditional counterparts. A fusion model sequentially connecting a graph neural network (GNN) and a convolutional neural network (CNN) to predict protein-ligand binding affinity is proposed in this work. In this model, the intermediate outputs of the GNN layers, as supplementary descriptors of atomic chemical environments at different levels, are concatenated with the input features of CNN. The model demonstrates a noticeable improvement in performance on CASF-2016 benchmark compared to its constituent CNN models. The generalization ability of the model is evaluated by setting a series of thresholds for ligand extended-connectivity fingerprint similarity or protein sequence similarity between the training and test sets. Masking experiment reveals that model can capture key interaction regions. Furthermore, the fusion model is applied to a virtual screening task for a novel target, PI5P4Kα. The fusion strategy significantly improves the ability of the constituent CNN model to identify active compounds. This work offers a novel approach to enhancing the accuracy of deep learning models in predicting binding affinity through fusion strategies., (© 2024 Wiley Periodicals LLC.)

Inhalable formulations of medicines intended to act locally in the lung are therapeutically effective at lower doses with targeted delivery, compared to parenteral or oral administration. Meanwhile, different APIs, including biologics, have proven to be challenging regarding formulation and final bioavailability. This study focuses on the production, improved stability performance, and delivery of spray-dried, inhalable protein powders to the lungs. By spray-drying 11 aqueous formulations of proteinX with varying L-leucine content and by employing a Design of Experiment (DoE), two formulations have been selected for stability studies based on the highest fine particle fraction (FPF), highest monomer content, and lowest particle size. We found that 5 %w/w L-leucine (based on protein content) resulted in similar or higher FPF at 2-8 °C and 25 °C/60 %RH (67.12 % and 48.50 %) stored for six months than 10 %w/w L-leucine (68.49 % and 35.04 %). This indicates that less leucine may be sufficient to produce stable, spray-dried inhalable particles with an improved FPF, and by doubling the leucine content, the aerosolization stability can deteriorate. We have discussed the postulated hypothesis underlying the observed stability behavior based on solid-state and morphological analysis. Our results suggest that spray-dried proteinX-leu-powders can be delivered to the lung at a lower dose than for intravenous administration., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Amrit Paudel reports financial support was provided by Research Center Pharmaceutical Engineering GmbH. Amrit Paudel reports a relationship with Research Center Pharmaceutical Engineering GmbH that includes: employment. Nothing to declare If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Hinge sites of proteins play a key role in mediating conformational mechanics. Among them, those involved in the most collective modes of motion, also called global hinges, are of particular interest, as they support cooperative rearrangements that are often functional. Yet, the utility of targeting global hinges for modulating function remains to be established. We present here a systematic study of a series of proteins resolved in drug-bound forms to examine the probabilistic occurrence of spatial overlaps between hinge sites and drug-binding pockets. Our analysis reveals a high propensity of drug binding to hinge sites compared to random. Notably, one-third of currently approved drugs are colocalized with hinge sites. These mechanosensitive sites are predictable by simple models such as the Gaussian Network Model. Their targeting thus emerges as a viable strategy for developing a new class of drugs that would exploit and modulate the target proteins' intrinsic dynamics, and potentially alleviate drug-resistance when used in combination with orthosteric or allosteric drugs., Competing Interests: Competing interests statement:The authors declare no competing interest.

Protein folding and unfolding experiments are interpreted under the assumption of microscopic reversibility, that is, that at equilibrium one process is the reverse of the other. Single-domain proteins illustrate the validity of such an interpretation, although reversibility does not necessarily hold under the different conditions typically used for folding and unfolding experiments. In fact, more complex proteins, which often exhibit irreversible unfolding, are generally considered not amenable to folding kinetics studies. Here, the X11 PDZ1-PDZ2 tandem repeat allows us to reveal the different folding and unfolding pathways at play under different experimental conditions, thus reconciling the apparent contradiction between theory and experiment., (© 2024 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

Functional groups in the side-chains of at least 10 amino acids are mainly involved in tertiary interactions. However, structural and functional significance of intra-residue interactions has not been fully recognized. In this study, we have analyzed ~5800 non-redundant high-resolution protein structures and identified 1166 self-contacts between the side-chain S-H/O-H and backbone C=O groups in Cys, Ser, and Thr residues that satisfied the geometric criteria to form hydrogen bonds. Quantum chemical calculations using model compounds were used to evaluate single point energy for 45 representative examples from different allowed regions of Ramachandran map. Relative energy profiles obtained by varying the side-chain dihedral angle χ 1 revealed that the energy difference between the crystal structure and the minimum energy conformations is between 0 and 3 kcal/mol. Natural bond orbital analysis (NBO) of self-contacting Cys residues revealed no charge transfer between Cys side-chain S-H and the backbone C=O groups. However, side-chain hydroxyl and the backbone C=O groups of 90%-95% of all self-contacting Ser and Thr residues are involved in charge transfer and the second order perturbation energy of majority of them is above 1 kcal/mol. Interaction energies calculated for model compounds along with NBO and NCIPLOT analyses demonstrate that the self-contacts observed in Ser and Thr residues can be described as hydrogen bonds. These interactions may provide stability to the loop/coil conformations. Self-contacting Cys residues are buried and the self-contacts appear to be mostly due to tertiary constraints. Dispersion between the self-contacting groups is one way to explain the close approach in Cys residues. Mutation studies will further validate and reveal the structural and functional significance of these self-contacting residues., (© 2024 The Protein Society.)

Machine learning (ML) constructs predictive models by understanding the relationship between protein sequences and their functions, enabling efficient identification of protein sequences with high fitness values without falling into local optima, like directional evolution. However, how to extract the most pertinent functional feature information from a limited number of protein sequences is vital for optimizing the performance of ML models. Here, we propose scut_ProFP (Protein Fitness Predictor), a predictive framework that integrates feature combination and feature selection techniques. Feature combination offers comprehensive sequence information, while feature selection searches for the most beneficial features to enhance model performance, enabling accurate sequence-to-function mapping. Compared to similar frameworks, scut_ProFP demonstrates superior performance and is also competitive with more complex deep learning models-ECNet, EVmutation, and UniRep. In addition, scut_ProFP enables generalization from low-order mutants to high-order mutants. Finally, we utilized scut_ProFP to simulate the engineering of the fluorescent protein CreiLOV and highly enriched mutants with high fluorescence based on only a small number of low-fluorescence mutants. Essentially, the developed method is advantageous for ML in protein engineering, providing an effective approach to data-driven protein engineering. The code and datasets for scut_ProFP are available at https://github.com/Zhang66-star/scut_ProFP., (© 2024 The Protein Society.)

Protein-ligand interactions represent an essential step to understand the bases of molecular recognition, an intense field of research in many scientific areas. Structural biology has played a central role in unveiling protein-ligand interactions, but current techniques are still not able to reliably describe the interactions of ligands with highly flexible regions. In this work, we explored the capacity of AlphaFold2 (AF2) to estimate the presence of interactions between ligands and residues belonging to disordered regions. As these interactions are missing in the crystallographic-derived structures, we called them "ghost interactions". Using a set of protein structures experimentally obtained after AF2 was trained, we found that the obtained models are good predictors of regions associated with order-disorder transitions. Additionally, we found that AF2 predicts residues making ghost interactions with ligands, which are mostly buried and show differential evolutionary conservation with the rest of the residues located in the flexible region. Our findings could fuel current areas of research that consider, given their biological relevance and their involvement in diseases, intrinsically disordered proteins as potentially valuable targets for drug development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Mucoadhesion is a special case of bioadhesion in which a material adheres to soft mucosal tissues. This review elucidates our current understanding of mucoadhesion across length, time, and energy scales by focusing on relevant structural features of mucus. We highlight the importance of both covalent and non-covalent interactions that can be tailored to maximize mucoadhesive interactions, particularly concerning proteinaceous mucoadhesives, which have been explored only to a limited extent so far in the literature. In particular, we highlight the importance of thiol groups, hydrophobic moieties, and charged species inherent to proteins as key levers to fine tune mucoadhesive performance. Some aspects of protein surface modification by grafting specific functional groups or coupling with polysaccharides to influence mucoadhesive performance are examined. Insights from this review offer a physicochemical roadmap to inform the development of biocompatible, protein-based mucoadhesive systems that can fulfil dual roles for both adhesion and delivery of actives, enabling the fabrication of advanced biomedical, nutritional and allied soft material technologies., Competing Interests: Declaration of competing interest None., (Copyright © 2024. Published by Elsevier B.V.)

Thermal proteome profiling (TPP) is an innovative technique that uses the principle of protein thermal stability to identify potential protein interaction partners. Employing quantitative mass spectrometry, TPP measures protein stability across the proteome, offering a comprehensive snapshot of protein interactions in a single experiment. When studying protein-protein interactions (PPI), TPP leverages changes in apparent protein melting temperatures to identify transient and weak interactions that most traditional PPI detection methodologies struggle to measure. This review discusses current TPP methodologies, the challenges of interpreting the resulting complex datasets, and opportunities to deepen and improve PPI networks. By advancing our grasp of intricate protein interactions, TPP promises to illuminate the molecular basis of diseases and drive the discovery of novel therapeutic targets., Competing Interests: Declaration of competing interest BCS is a founder and shareholder in Proteome Software, which operates in the field of proteomics., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Biomolecular condensates appear throughout cell physiology and pathology, but the specific role of condensation or its dynamics is often difficult to determine. Optogenetics offers an expanding toolset to address these challenges, providing tools to directly control condensation of arbitrary proteins with precision over their formation, dissolution, and patterning in space and time. In this review, we describe the current state of the field for optogenetic control of condensation. We survey the proteins and their derivatives that form the foundation of this toolset, and we discuss the factors that distinguish them to enable appropriate selection for a given application. We also describe recent examples of the ways in which optogenetic condensation has been used in both basic and applied studies. Finally, we discuss important design considerations when engineering new proteins for optogenetic condensation, and we preview future innovations that will further empower this toolset in the coming years., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Salt-tolerant proteins, also known as halophilic proteins, have unique adaptations to function in high-salinity environments. These proteins have naturally evolved in extremophilic organisms, and more recently, are being increasingly applied as enzymes in industrial processes. Due to an abundance of salt-tolerant sequences and a simultaneous lack of experimental structures, most computational methods to predict stability are sequence-based only. These approaches, however, are hindered by a lack of structural understanding of these proteins. Here, we present HaloClass, an SVM classifier that leverages ESM-2 protein language model embeddings to accurately identify salt-tolerant proteins. On a newer and larger test dataset, HaloClass outperforms existing approaches when predicting the stability of never-before-seen proteins that are distal to its training set. Finally, on a mutation study that evaluated changes in salt tolerance based on single- and multiple-point mutants, HaloClass outperforms existing approaches, suggesting applications in the guided design of salt-tolerant enzymes., (© 2024. The Author(s).)

Ultrasonic and microwave extraction process has great prospects to convert food and agricultural waste from food industries to value-added goods. Also, this review extensively elaborates the utilization of ultrasonication and microwave extraction (US-MW) process for valorization of dairy waste extracted proteins into novel foods. Both of these extraction and processing techniques are considered as green technologies when compared with the other conventional or chemical extraction and processing techniques. Further, this review also explains the impact of US-MW alone and in combination on the dairy waste proteins extraction, nutritional and techno-functional attributes of these dairy-waste proteins. The review also highlights the economic and cost-effective benefits of US-MW processes for extracting the proteins from dairy waste, indicating their feasibility and sustainability. The review also elucidated the synergistic utilization of US-MW extraction as a viable processing technique in extraction or production of bioactive compounds like dairy proteins. In conclusion, this review elucidates the US-MW, both individually and in synergy as a viable source of dairy waste proteins extraction and their application in functional foods. Moreover, in accordance to the latest developments and future prospects at pilot and commercial level to assess the practicability of synergistic use of US-MW extraction in bioenergy production from food wastes other than dairy waste for extraction and production of biodiesel, hydrogen, green methane, and ethanol., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

This research developed a bio-based adhesive (AP) derived from industrial slaughterhouse waste, comprising over 85% protein. The adhesive was characterized by a melting point of 193.14 °C, a neutral pH of 7, and a viscosity comparable to common wood adhesives such as urea-formaldehyde and phenol-formaldehyde. Utilizing this adhesive, a Rhizophora spp. particleboard phantom was produced, featuring wood particles of ≤149 μm, an adhesive concentration of 12%, and a target density of 1 g/cm³, adhering to a standard phantom dimension of 30 cm × 30 cm × 30 cm. The dosimetric properties of this particleboard phantom were subsequently compared with those of water and Perspex phantoms within an X-ray energy range of 60-100 kVp, employing high-sensitivity thermo-luminescent dosimeters (TL-100H). The findings indicated that the percentage depth dose (PDD) values of the AP-Rhizophora spp. particleboard were closely aligned with those of the Perspex and water phantoms, with the greatest discrepancy observed at 60 kVp. Additionally, the half-value layer (HVL) of the particleboard was similar to those of Perspex and water, particularly at diagnostic X-ray energies. These results demonstrate that the AP adhesive is effective for creating Rhizophora spp. particleboard phantoms, exhibiting dosimetric properties comparable to tissue-equivalent materials., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests., (Copyright © 2024. Published by Elsevier Ltd.)

Synchrotron radiation is extensively utilized in the domains of materials science, physical chemistry, and life science, resulting from its high intensity, exceptional monochromaticity, superior collimation, and broad wave spectrum. This top-notch light source has also made significant contributions to the progress of biomedicine. The advancement of synchrotron radiation-based X-ray and protein crystallography technologies has created new prospects for drug discovery. These innovative techniques have opened up exciting avenues in the field. The investigation of protein crystal structures and the elucidation of the spatial configuration of biological macromolecules have revealed intricate details regarding the modes of protein binding. Furthermore, the screening of crystal polymorphs and ligands has laid the groundwork for rational drug modification and the improvement of drug physicochemical properties. As science and technology continue to advance, the techniques for analyzing structures using synchrotron radiation sources and the design of corresponding crystallographic beamline stations are undergoing continuous enhancement. These cutting-edge tools and facilities are expected to expedite the drug development process and rectify the current situation of a lack of targeted drugs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Natural proteins are fragile entities, intrinsically sensitive to perturbations both at the level of sequence and their immediate environment. Here, we highlight the diverse strategies available for engineering function through mutations influencing backbone conformational entropy, charge-charge interactions, and in the loops and hinge regions, many of which are located far from the active site. It thus appears that there are potentially numerous ways to microscopically vary the identity of residues and the constituent interactions to tune function. Functional modulation could occur via changes in native-state stability, altered thermodynamic coupling extents within the folded structure, redistributed dynamics, or through modulation of the population of conformational substates. As these mechanisms are intrinsically linked and given the pervasive long-range effects of mutations, it is crucial to consider the interaction network as a whole and fully map the native conformational landscape to place mutational effects in the context of allostery and protein evolution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Understanding how proteins behave dynamically and undergo conformational changes is essential to comprehending their biological roles. This review article examines the potent tool of using Molecular Dynamics simulations in conjunction with Principal Component Analysis (PCA) to explore protein dynamics. Molecular dynamics data can be made easier to read by removing prominent patterns through the use of PCA, a sophisticated dimensionality reduction approach. Researchers can obtain critical insights into the fundamental principles governing protein function by using PCA on MD simulation data. We provide a systematic approach to PCA that includes data collection, input coordinate selection, and result interpretation. Protein collective movements and fundamental dynamics are made visible by PCA, which makes it possible to identify conformational substates that are crucial to function. By means of principal component analysis, scientists are able to observe and measure large-scale movements, like hinge bending and domain motions, as well as pinpoint areas of protein structural stiffness and flexibility. Moreover, PCA allows temporal separation, distinguishing slower global motions from faster local changes. A strong foundation for researching protein dynamics is provided by the combination of PCA and Molecular Dynamics simulations, which have applications in drug development and enhance our comprehension of intricate biological systems., Competing Interests: Declaration of competing interest The authors confirm that there are no any conflicts of interests to be declared., (Copyright © 2024. Published by Elsevier Ltd.)

Molecular dynamics (MD) simulations are important tools for studying the dynamic motions of macromolecules at the atomic level. With the increasing capabilities of high performance computing, MD simulations are becoming more widely used. This allows molecular modelers to simulate the molecular behavior of large molecular architectures for much longer trajectories. Appropriate visualization of MD trajectories is becoming essential to provide an immediate and intuitive understanding of a molecule's dynamics and function. In this study, we implement a novel 3D graphical representation, Dynamical Sheets Helper for RepresentAtion of SurfAce Descriptors (Dy-SheHeRASADe), to visualize the β sheet secondary structures of proteins in the context of molecular dynamics. Dy-SheHeRASADe is developed in UnityMol, an open source molecular viewer and prototyping platform. We considered β sheet fluctuations and hydrogen bond formation during molecular dynamics simulations to characterize the parts of β sheets with large motions or with labile bonds. We propose two visualization modes based on a surface representation of the β sheets calculated according to the positions of the α carbons and the hydrogen bonds between the β strands. The volumetric mode, in which this surface is enclosed in a semi-transparent volume that represents the fluctuation zone of the sheet during dynamics. The heatmap mode, in which the surface is colored according to the amplitude values of the α carbons. In addition, we quantify the β sheet fluctuations by displaying the values of the largest and smallest movements of the β sheets, the surface area of the sheets, and the number of hydrogen bonds., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Protein nitrotyrosine is an essential post-translational modification that results from the nitration of tyrosine amino acid residues. This modification is known to be associated with the regulation and characterization of several biological functions and diseases. Therefore, accurate identification of nitrotyrosine sites plays a significant role in the elucidating progress of associated biological signs. In this regard, we reported an accurate computational tool known as iNTyro-Stack for the identification of protein nitrotyrosine sites. iNTyro-Stack is a machine-learning model based on a stacking algorithm. The base classifiers in stacking are selected based on the highest performance. The feature map employed is a linear combination of the amino composition encoding schemes, including the composition of k-spaced amino acid pairs and tri-peptide composition. The recursive feature elimination technique is used for significant feature selection. The performance of the proposed method is evaluated using k-fold cross-validation and independent testing approaches. iNTyro-Stack achieved an accuracy of 86.3% and a Matthews correlation coefficient (MCC) of 72.6% in cross-validation. Its generalization capability was further validated on an imbalanced independent test set, where it attained an accuracy of 69.32%. iNTyro-Stack outperforms existing state-of-the-art methods across both evaluation techniques. The github repository is create to reproduce the method and results of iNTyro-Stack, accessible on: https://github.com/waleed551/iNTyro-Stack/., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest related to this research., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

Food security for the increasing global population is a significant challenge of the current times particularly highlighting the protein deficiencies. Plant-based proteins could be considered as alternate source of the protein. The digestibility and PDCASS value of these proteins are still a concern. Blue proteins, the new approach of utilizing the proteins from aquatic sources could be a possible solution as it contains all the essential amino acids. However, the conjugation of these proteins with fats and glycogen interferes with their techno-functional properties and consequently their applicability. The application of power ultrasound for extraction and modification of these proteins from aquatic sources to break open the cellular structure, increase extractability, alter the protein structure and consequently provide proteins with higher bioavailability and bioactive properties could be a potential approach for their effective utilization into food systems. The current review focuses on the application of power ultrasound when applied as extraction treatment, alters the sulphite and peptide bond and modifies protein to elevated digestibility. The degree of alteration is influenced by intensity, frequency, and exposure time. The extracted proteins will serve as a source of essential amino acids. Furthermore, modification will lead to the development of bioactive peptides with different functional applications. Numerous studies reveal that blue proteins have beneficial impacts on amino acid availability, and subsequently food security with higher PDCAAS values. In many cases, converted peptides give anti-hypertensive, anti-diabetic, and anti-oxidant activity. Therefore, researchers are concentrating on ultrasound-based extraction, modification, and application in food and pharmaceutical systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Conditional protein-protein interactions enable dynamic regulation of cellular activity and are an attractive approach to probe native protein interactions, improve metabolic engineering of microbial factories, and develop smart therapeutics. Conditional protein-protein interactions have been engineered to respond to various chemical, light, and nucleic acid-based stimuli. These interactions have been applied to assemble protein fragments, build protein scaffolds, and spatially organize proteins in many microbial and higher-order hosts. To foster the development of novel conditional protein-protein interactions that respond to new inputs or can be utilized in alternative settings, we provide an overview of the process of designing new engineered protein interactions while showcasing many recently developed computational tools that may accelerate protein engineering in this space., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Residue interaction networks (RINs) provide graph-based representations of interaction networks within proteins, providing important insight into the factors driving protein structure, function, and stability relationships. There exists a wide range of tools with which to perform RIN analysis, taking into account different types of interactions, input (crystal structures, simulation trajectories, single proteins, or comparative analysis across proteins), as well as formats, including standalone software, web server, and a web application programming interface (API). In particular, the ability to perform comparative RIN analysis across protein families using "metaRINs" provides a valuable tool with which to dissect protein evolution. This, in turn, highlights hotspots to avoid (or target) for in vitro evolutionary studies, providing a powerful framework that can be exploited to engineer new proteins., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

This research aims to investigate the mechanism of the effect of intrinsic and non-intrinsic protein content on the stability of oil bodies (OBs) emulsion gels. We employed small amplitude oscillation shear (SAOS) and large amplitude oscillation shear (LAOS) to measure the linear and nonlinear rheological properties of the OBs emulsion gels. The SAOS test indicated that an increase in non-intrinsic protein content weakened the interaction between OBs, decreasing their storage modulus (G'). The LAOS test demonstrated that the increase in non-intrinsic protein content affected the structural recombination and destruction behavior of OBs emulsion gels under large strains. Overall, the content of non-intrinsic protein during the extraction process is a crucial factor affecting the stability of OBs emulsion gels. These findings provide insights into the potential strategies for improving oil extraction efficiency and offer a foundation for further investigation into the functional properties of OBs., Competing Interests: Declaration of competing interest Conflict of interest statement We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled: Investigating viscoelastic properties and structural stability mechanisms of oil bodies emulsion gels: role of non-intrinsic protein., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

When two proteins bind to each other, this process is often accompanied by a change in their structural states (from disordered to ordered or vice versa). As it turns out, there are 10 distinct possibilities for such binding-related order/disorder transitions. Out of this number, seven scenarios have been experimentally observed, while another three remain hitherto unreported. As an example, we discuss the so-called mutual synergistic folding, whereby two disordered proteins come together to form a fully structured complex. Our bioinformatics analysis of the Protein Databank found potential new examples of this remarkable binding mechanism., (© 2024 The Author(s). PROTEINS: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.)

X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination. In this study, we presented a novel deep learning framework, PLMC, to improve multi-stage protein crystallization propensity prediction by leveraging a pre-trained protein language model. To effectively train PLMC, two groups of features of each protein were integrated into a more comprehensive representation, including protein language embeddings from the large-scale protein sequence database and a handcrafted feature set consisting of physicochemical, sequence-based and disordered-related information. These features were further separately embedded for refinement, and then concatenated for the final prediction. Notably, our extensive benchmarking tests demonstrate that PLMC greatly outperforms other state-of-the-art methods by achieving AUC scores of 0.773, 0.893, and 0.913, respectively, at the aforementioned individual stages, and 0.982 at the final crystallization stage. Furthermore, PLMC is shown to be superior for predicting the crystallization of both globular and membrane proteins, as demonstrated by an AUC score of 0.991 for the latter. These results suggest the significant potential of PLMC in assisting researchers with the experimental design of crystallizable protein variants., (© 2024. International Association of Scientists in the Interdisciplinary Areas.)

The effects of adsorption behavior and assembly mechanism of proteins and lipids at the interface on the formation of yuba films were investigated. The thickness of yuba films increased rapidly from nano to micro scale within minutes according to the scanning electron microscopy (SEM) images. The confocal laser scanning microscope (CLSM), SEM images, and sodium dodecyl sulfate-polyacrylamide gel electrophoresis revealed that the formation of protein aggregates (40-100 nm) was an essential requirement for the development of yuba. Meanwhile, a relatively loose spatial structure was formed by protein aggregates under the influence of water vapor. This structure served as the foundation for incorporating lipids. Interfacial adsorption kinetics indicated that increasing the concentration (from 3 to 9 mg/mL) of protein aggregates enhanced the rearrangement rate. This finding demonstrated that the variations of interfacial protein aggregate concentration were a crucial factor leading to the non-linear growth of film thickness., Competing Interests: Declaration of competing interest The authors declare that they do not possess any recognized competing financial interests or personal relationships that could have potentially influenced the findings presented in this research., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels of generalization capability and interpretability. Intuitively, the inductive bias of an ML-based model for PLA prediction should fit in with biological mechanisms relevant for binding to achieve good predictions with meaningful reasons. To this end, we propose an interaction-based inductive bias to restrict neural networks to functions relevant for binding with two assumptions: 1) A protein-ligand complex can be naturally expressed as a heterogeneous graph with covalent and non-covalent interactions; 2) The predicted PLA is the sum of pairwise atom-atom affinities determined by non-covalent interactions. The interaction-based inductive bias is embodied by an explainable heterogeneous interaction graph neural network (EHIGN) for explicitly modeling pairwise atom-atom interactions to predict PLA from 3D structures. Extensive experiments demonstrate that EHIGN achieves better generalization capability than other state-of-the-art ML-based baselines in PLA prediction and structure-based virtual screening. More importantly, comprehensive analyses of distance-affinity, pose-affinity, and substructure-affinity relations suggest that the interaction-based inductive bias can guide the model to learn atomic interactions that are consistent with physical reality. As a case study to demonstrate practical usefulness, our method is tested for predicting the efficacy of Nirmatrelvir against SARS-CoV-2 variants. EHIGN successfully recognizes the changes in the efficacy of Nirmatrelvir for different SARS-CoV-2 variants with meaningful reasons.

Fried oyster is a popular aquatic food product in East Asia, but nutrient loss during thermal processing become a significant concern. The goal of this research was to examine the impact of distinct frying techniques, including deep frying (DF), air frying (AF), and vacuum frying (VF), on the nutritional, textural and flavor characteristics of oysters. The VF method demonstrated superior retention of beneficial properties and flavor, and reduced protein and lipid oxidation compared to the DF and AF methods. Furthermore, proteomic analysis of oysters was attempted to explain the molecular mechanisms governing the influence of key differential proteins. 20 major differential proteins, including actin-2 protein, tryptophan 2,3-dioxygenase and 1-alph, involved in oyster protein oxidation were identified, annotated and analyzed to elucidate their influence mechanisms. This research provides a deeper understanding of intricate interactions between frying techniques and oyster biochemistry, which offers valuable implications for enhancing food quality in seafood industry., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This work investigated the effects of five thawing methods (air thawing (AT), water thawing (WT), plasma-activated water thawing (PT), ultrasound-assisted water thawing (UWT) and ultrasound-assisted plasma-activated water thawing (UPT)) on thawing rate, quality characteristics, lipid and protein oxidation of porcine longissimus dorsi using fresh sample as control. The thawing time of UPT samples was significantly reduced by 81.15% compared to AT treatment (P < 0.05). The thawing loss of UPT samples was 1.55% significantly lower than AT samples (4.51%) (P < 0.05). In addition, UPT samples had the least cooking loss and centrifugal loss. UPT treatment reduced the conversion of bound and immobilized water to free water and resulted in more uniform water distribution. UPT treatment significantly decreased the thiobarbituric acid reactive substances (TBARS) value and carbonyl content and increased the total sulfhydryl content of the samples (P < 0.05). In conclusion, UPT treatment increased the thawing rate and retarded the lipid and protein oxidation, resulting in better maintenance of quality characteristics of porcine longissimus dorsi than other thawing methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Last decade has witnessed impressive progress in the fields of medicine and bioengineering with the aid of nanomaterials. Nanomaterials are favoured for their improved bio-chemical as well as mechanical properties with tremendous applications in biomedical domains such as disease diagnosis, targeted drug delivery, medical imaging, in vitro diagnostics, designing innovatory cross-functional implants and regenerative tissue engineering. The current situation insists upon crafting nanotools that are capable of catering to biological needs and construct more efficient biomedical strategies. In the recent years, surface functionalization and capping with biomolecules has initiated substantial interest towards research. In this regard, search of suitable biofunctionalized nanoparticles seem to be like finding pearls from ocean. Conjugating biological molecules with inorganic materials has paved the way for unravelling innovative functional materials with dramatically improved properties and a wide range of uses. Inorganic nanoparticles such as metals, metal oxides, as well as quantum dots have been hybridised or conjugated with biomolecules such as proteins, peptides, carbohydrates, and nucleic acids. The present review reports on various biomolecule functionalized inorganic nanomaterials highlighting the biomolecule-inorganic nanoparticle interaction studies, the mechanism of functionalization, antimicrobial efficacy of the functionalised nanoconjugates and its use in various biomedical applications.

Solenaia oleivora is a valuable freshwater mussel endemic to China with a high content of high-quality proteins, but the lack of structural information and limited functionality of Solenaia oleivora proteins constrained their application in the food industry. This study investigates the changes in structural characteristics and functionality of Solenaia oleivora protein under ultrasound processing at power from 200 to 600 W. The ultrasound treatment caused increased contents of β-turn and α-helix, and the exposure of interior hydrophobic groups, resulting in the increased hydrophobicity by around 3 folds. The ultrasound treatment could significantly decrease particle size and increase surface charges of Solenaia oleivora proteins, facilitating the increase of hydrosolubility from 10.2% to 81.7%. These structural changes and increased hydrosolubility contributed to the enhancement of emulsifying and foaming properties, and in vitro digestibility. The results suggested that the ultrasound-treated Solenaia oleivora proteins possessed the potential as an alternative protein in food applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Allostery is an intriguing phenomenon wherein the binding activity of a biological macromolecule is modulated via non-canonical binding site, resulting in synchronized functional changes. The mechanics underlying allostery are relatively complex and this review is focused on common methodologies used to study allostery, such as X-ray crystallography, NMR spectroscopy, and HDXMS. Different methodological approaches are used to generate data in different scenarios. For example, X-ray crystallography provides high-resolution structural information, NMR spectroscopy offers dynamic insights into allosteric interactions in solution, and HDXMS provides information on protein dynamics. The residue transition state (RTS) approach has emerged as a critical tool in understanding the energetics and conformational changes associated with allosteric regulation. Allostery has significant implications in drug discovery, gene transcription, disease diagnosis, and enzyme catalysis. Enzymes' catalytic activity can be modulated by allosteric regulation, offering opportunities to develop novel therapeutic alternatives. Understanding allosteric mechanisms associated with infectious organisms like SARS-CoV and bacterial pathogens can aid in the development of new antiviral drugs and antibiotics. Allosteric mechanisms are crucial in the regulation of a variety of signal transduction and cell metabolism pathways, which in turn govern various cellular processes. Despite progress, challenges remain in identifying allosteric sites and characterizing their contribution to a variety of biological processes. Increased understanding of these mechanisms can help develop allosteric systems specifically designed to modulate key biological mechanisms, providing novel opportunities for the development of targeted therapeutics. Therefore, the current review aims to summarize common methodologies that are used to further our understanding of allosteric mechanisms. In conclusion, this review provides insights into the methodologies used for the study of allostery, its applications in in silico modeling, the mechanisms underlying antibody allostery, and the ongoing challenges and prospects in advancing our comprehension of this intriguing phenomenon., Competing Interests: Declarations Competing interests The authors declare no competing interests. Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants with improved stability and activity without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on the public mutagenesis dataset across 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the impact of the top 30 to 45 single-site mutations on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize nonnatural nucleic acid or resilience to extreme alkaline conditions. More than 30% of PRIME-recommended mutants exhibited superior performance compared to their premutation counterparts across all proteins and desired properties. We developed an efficient and effective method based on PRIME to rapidly obtain multisite mutants with enhanced activity and stability. Hence, PRIME demonstrates broad applicability in protein engineering.

Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The viscosity and diffusion properties of crowded protein systems were investigated with molecular dynamics simulations of SH3 mixtures with different crowders, and results were compared with experimental data. The simulations accurately reproduced experimental trends across a wide range of protein concentrations, including highly crowded environments up to 300 g/L. Notably, viscosity increased with crowding but varied little between different crowder types, while diffusion rates were significantly reduced depending on protein-protein interaction strength. Analysis using the Stokes-Einstein relation indicated that the reduction in diffusion exceeded what was expected from viscosity changes alone, with the additional slow-down attributable to transient cluster formation driven by weakly attractive interactions. Contact kinetics analysis further revealed that longer-lived interactions contributed more significantly to reduced diffusion rates than short-lived interactions. This study also highlights the accuracy of current computational methodologies for capturing the dynamics of proteins in highly concentrated solutions and provides insights into the molecular mechanisms affecting protein mobility in crowded environments.

Quantum-chemical fragmentation methods offer an attractive approach for the accurate calculation of protein-ligand interaction energies. While the molecular fractionation with conjugate caps (MFCC) scheme offers a rather straightforward approach for this purpose, its accuracy is often not sufficient. Here, we upgrade the MFCC scheme for the calculation of protein-ligand interactions by including many-body contributions. The resulting fragmentation scheme is an extension of our previously developed MFCC-MBE(2) scheme [ J. Comput. Chem. 2023 , 44, 1634-1644]. For a diverse test set of protein-ligand complexes, we demonstrate that by upgrading the MFCC scheme with many-body contributions, the error in protein-ligand interaction energies can be reduced significantly, and one generally achieves errors below 20 kJ/mol. Our scheme allows for systematically reducing these errors by including higher-order many-body contributions. As it combines the use of single amino acid fragments with high accuracy, our scheme provides an ideal starting point for the parametrization of accurate machine learning potentials for proteins and protein-ligand interactions.

We successfully found that boroxazolidone can dissociate under aqueous conditions, which were applicable to a solution containing proteins. The immobilization of a newly synthesized diarylborinic acid on beads enabled the capture and release of target proteins of bioactive compounds through boroxazolidone formation and dissociation.

Noncanonical amino acids (ncAAs) are synthetic building blocks that, when incorporated into proteins, confer novel functions and enable precise control over biological processes. These small yet powerful tools offer unprecedented opportunities to investigate and manipulate various complex life forms. In particular, ncAA incorporation technology has garnered significant attention in the study of animals and their constituent cells, which serve as invaluable model organisms for gaining insights into human physiology, genetics, and diseases. This review will provide a comprehensive discussion on the applications of ncAA incorporation technology in animals and animal cells, covering past achievements, current developments, and future perspectives.

Artificial Intelligence (AI)-based deep learning methods for predicting protein structures are reshaping knowledge development and scientific discovery. Recent large-scale application of AI models for protein structure prediction has changed perceptions about complicated biological problems and empowered a new generation of structure-based hypothesis testing. It is well-recognized that proteins have a modular organization according to archetypal folds. However, it is yet to be determined if predicted structures are tuned to one conformation of flexible proteins or if they represent average conformations. Further, whether or not the answer is protein fold-dependent. Therefore, in this study, we analyzed 2878 proteins with at least ten distinct experimental structures available, from which we can estimate protein topological rigidity verses heterogeneity from experimental measurements. We found that AlphaFold v2 (AF2) predictions consistently return one specific form to high accuracy, with 99.68% of distinct folds (n = 623 out of 628) having an experimental structure within 2.5Å RMSD from a predicted structure. Yet, 27.70% and 10.82% of folds (174 and 68 out of 628 folds) have at least one experimental structure over 2.5Å and 5Å RMSD, respectively, from their AI-predicted structure. This information is important for how researchers apply and interpret the output of AF2 and similar tools. Additionally, it enabled us to score fold types according to how homogeneous versus heterogeneous their conformations are. Importantly, folds with high heterogeneity are enriched among proteins which regulate vital biological processes including immune cell differentiation, immune activation, and metabolism. This result demonstrates that a large amount of protein fold flexibility has already been experimentally measured, is vital for critical cellular processes, and is currently unaccounted for in structure prediction databases. Therefore, the structure-prediction revolution begets the protein dynamics revolution!, Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Haque et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The goal of this paper is predicting the conformational distributions of ligand binding sites using the AlphaFold2 (AF2) protein structure prediction program with stochastic subsampling of the multiple sequence alignment (MSA). We explored the opening of cryptic ligand binding sites in 16 proteins, where the closed and open conformations define the expected extreme points of the conformational variation. Due to the many structures of these proteins in the Protein Data Bank (PDB), we were able to study whether the distribution of X-ray structures affects the distribution of AF2 models. We have found that AF2 generates both a cluster of open and a cluster of closed models for proteins that have comparable numbers of open and closed structures in the PDB and not too many other conformations. This was observed even with default MSA parameters, thus without further subsampling. In contrast, with the exception of a single protein, AF2 did not yield multiple clusters of conformations for proteins that had imbalanced numbers of open and closed structures in the PDB, or had substantial numbers of other structures. Subsampling improved the results only for a single protein, but very shallow MSA led to incorrect structures. The ability of generating both open and closed conformations for six out of the 16 proteins agrees with the success rates of similar studies reported in the literature. However, we showed that this partial success is due to AF2 "remembering" the conformational distributions in the PDB and that the approach fails to predict rarely seen conformations., Competing Interests: Competing interests statement:The authors declare no competing interest.

All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities.

Sugar molecules play important roles as mediators of biomolecular interactions in cellular functions, disease, and infections. Molecular dynamics simulations are an indispensable tool to explore these interactions at the molecular level. The large time and length scales involved frequently necessitate the use of coarse-grained representations, which heavily depend on the parametrization of sugar-protein interactions. Here, we adjust the sugar-protein interactions in the widely used Martini 2.2 force field to reproduce the experimental osmotic second virial coefficients between sugars and proteins. In simulations of two model proteins in glucose solutions with adjusted force field parameters, we observe weak protein-sugar interactions. The sugar molecules are thus acting mainly as crowding agents, in agreement with experimental measurements. The procedure to fine-tune sugar-protein interactions is generally applicable and could also prove useful for atomistic force fields.

Dynamic or structurally induced ionization is a critical aspect of many physical, chemical, and biological processes. Molecular dynamics (MD) based simulation approaches, specifically constant pH MD methods, have been developed to simulate ionization states of molecules or proteins under experimentally or physiologically relevant conditions. While such approaches are now widely utilized to predict ionization sites of macromolecules or to study physical or biological phenomena, they are often computationally expensive and require long simulation times to converge. In this article, using the principles of adiabatic free energy dynamics, we introduce an efficient technique for performing constant pH MD simulations within the framework of the adiabatic free energy dynamics (AFED) approach. We call the new approach pH-AFED. We show that pH-AFED provides highly accurate predictions of protein residue p K a values, with a MUE of 0.5 p K a units when coupled with driven adiabatic free energy dynamics (d-AFED), while reducing the required simulation times by more than an order of magnitude. In addition, pH-AFED can be easily integrated into most constant pH MD codes or implementations and flexibly adapted to work in conjunction with enhanced sampling algorithms that target collective variables. We demonstrate that our approaches, with both pH-AFED standalone as well as pH-AFED combined with collective variable based enhanced sampling, provide promising predictive accuracy, with a MUE of 0.6 and 0.5 p K a units respectively, on a diverse range of proteins and enzymes, ranging up to 186 residues and 21 titratable sites. Lastly, we demonstrate how this approach can be utilized to understand the in vivo performance engineered antibodies for immunotherapy.

We show that PEG-induced macromolecular crowding enhances protein detection in nanopores by increasing capture rate and translocation frequency. Experimental data indicate that a PEG concentration gradient boosts capture efficiency, while our theoretical model attributes this enhancement to osmotic flow, offering insights for improving nanopore-based biosensing.

Protein folding, which traces the protein three-dimensional (3D) structure from its amino acid sequence, is a half-a-century-old problem in biology. The function of the protein correlates with its structure, emphasizing the need to study protein folding to understand the cellular and molecular processes better. While recent AI-based methods have shown significant success in protein structure prediction, their accuracy diminishes with proteins of low sequence similarity. Classical simulations face challenges in generating extensive conformational samplings. In this work, we develop a novel turn-based encoding algorithm with more significant degrees of freedom that successfully runs on a gate-based quantum computer and predicts the structure of proteins of varied lengths utilizing up to 114 qubits (IBM hardware). To make the problem tractable in quantum computers, the protein sequences were described with the simplistic HP model (H = hydrophobic residues, P = polar residues). The proposed formulation successfully captures the so-called nucleation step in protein folding, the hydrophobic collapse, that brings the hydrophobic residues to the core of the protein.

Supramolecular chemistry achieves higher-order molecular self-assembly through non-covalent interactions. Utilizing supramolecular methods to explore the polymorphism of proteins, the building blocks of life, from a "bottom-up" perspective is essential for constructing diverse and functional biomaterials. In recent years, significant progress has been achieved in the design strategies and functional applications of supramolecular protein self-assembly, becoming a focal point for researchers. This paper reviews classical supramolecular strategies driving protein self-assembly, including electrostatic interactions, metal coordination, hydrogen bonding, hydrophobic interactions, host-guest interactions, and other mechanisms. We discuss how these supramolecular interactions regulate protein assembly processes and highlight protein supramolecular assemblies' unique structural and functional advantages in constructing artificial photosynthetic systems, protein hydrogels, bio-delivery systems, and other functional materials. The enormous potential and significance of supramolecular protein materials are elucidated. Finally, the challenges in preparing and applying protein supramolecular assemblies are summarized, and future development directions are projected., (© 2024 Wiley-VCH GmbH.)

In recent years, the application of deep learning models to protein-ligand docking and affinity prediction, both vital for structure-based drug design, has garnered increasing interest. However, many of these models overlook the intricate modeling of interactions between ligand and protein atoms in the complex, consequently limiting their capacity for generalization and interpretability. In this work, we propose Interformer, a unified model built upon the Graph-Transformer architecture. The proposed model is designed to capture non-covalent interactions utilizing an interaction-aware mixture density network. Additionally, we introduce a negative sampling strategy, facilitating an effective correction of interaction distribution for affinity prediction. Experimental results on widely used and our in-house datasets demonstrate the effectiveness and universality of the proposed approach. Extensive analyses confirm our claim that our approach improves performance by accurately modeling specific protein-ligand interactions. Encouragingly, our approach advances docking tasks state-of-the-art (SOTA) performance., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. De novo protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent years, with the rapid development of artificial intelligence (AI), deep learning-based generative models have increasingly become powerful tools, enabling the design of functional proteins with atomic-level precision. This article provides an overview of the evolution of de novo protein design, with focus on the latest algorithmic models, and then analyzes existing challenges such as low design success rates, insufficient accuracy, and dependence on experimental validation. Furthermore, this article discusses the future trends in protein design, aiming to provide insights for researchers and practitioners in this field.

Molecular docking is an essential tool in structure-based drug discovery, widely utilized to model ligand-protein interactions and enrich potential hits. Among the different docking strategies, semiflexible docking (rigid-receptor and flexible-ligand model) is the most popular, benefiting from its balance of docking accuracy and speed. However, this approach ignores the conformational changes of proteins and hence demands suitable protein conformations as input. When the binding interaction adheres to an induced-fit model, flexible methods such as molecular dynamics simulation can be utilized, but they are computationally demanding. To balance between speed and accuracy, the flexible docking approach is an effective choice, as exemplified by AutoDock Vina and AutoDockFR, which treat selected protein side chains as flexible parts. However, the efficiency of flexible docking methods is yet to be improved for virtual screening usage. In this article, we introduce DSDPFlex, an improved flexible-receptor docking method accelerated by GPU parallelization. Beyond acceleration, optimizations with respect to sampling, scoring, and search space are implemented in DSDPFlex to further improve its capability in flexible tasks. In cross-docking evaluation, DSDPFlex demonstrates superior accuracy compared to AutoDock Vina and is 100 times faster than Vina in flexible-receptor tasks. We also show the advantage of flexible-receptor methods on suboptimal pockets and validate the advantage of DSDPFlex in screening on apo and AlphaFold2-predicted structures. With improvements in both efficiency and accuracy, DSDPFlex is expected to hold potential in future docking-based studies.